Hypertrophic cardiomyopathy (HCM) is the most common monogenic heart disease and the most frequent cause of sudden cardiac death (SCD) in the young. It is characterized by unexplained left ventricular hypertrophy (LVH), diffuse and patchy fibrosis, and myofibrillar disarray. While the majority of patients remain asymptomatic, prognosis is poor in a subset who present with SCD or progress to heart failure (HF). Current methods to predict risk of these adverse events and to target therapy are limited. Current medical therapy does not protect against SCD, nor does it prevent development of HF. Therefore, the identification of novel risk markers would help develop therapeutic targets aimed at altering the phenotypic expression to impact the natural history, especially SCD and HF. Cardiovascular magnetic resonance (CMR) is emerging as a powerful tool for diagnosis and risk stratification in HCM including assessment of LV mass and pattern of hypertrophy. Late gadolinium enhancement by CMR is a marker of focal myocardial fibrosis which is thought to underlie the arrhythmogenic substrate as well as promote development of HF. The investigators hypothesize that HCM patients with a higher primary outcome event rate can be identified by novel CMR findings. The majority of cases of HCM are autosomal dominant and about 60% are caused by mutations in genes encoding cardiac sarcomeric proteins. However, the relationship between genetic mutation, disease phenotype, and clinical outcomes remains poorly understood. The investigators hypothesize that HCM patients with sarcomeric HCM mutations will have a higher primary outcome event rate and more marked myocardial pathology on CMR than those without. Furthermore, there may be a link between sarcomeric mutations and fibrosis, as mutation carriers with overt HCM as well as those without hypertrophy have elevated markers of collagen turnover. The investigators therefore hypothesize that serum biomarkers of collagen metabolism in HCM will predict outcomes. Thus, the Specific Aim is to develop a predictive model of cardiovascular outcomes in HCM by: 1) using exploratory data mining methods to identify demographic, clinical, and novel CMR, genetic and biomarker variables associated with the outcomes and 2) develop a score from the predictive model that can be used to assess risk given a patient's combination of risk factors, thus establishing the evidence base to enable clinical trial design to reduce morbidity and mortality in HCM in a cost-effective manner.

2.2 Rationale Presently used risk predictors in HCM for the clinical outcomes of SCD and HF are still insufficient and limit clinical trials and institution of novel therapies in this disease. This large scale, prospective clinical registry will systematically answer the important question whether, through addition of a combination of advanced CMR phenotyping, genetic and biomarker analysis, risk stratification in HCM could be substantially improved over presently used clinical risk predictors. Emerging novel blood, genetic, and CMR markers offer the paradigm-shifting promise of reliably identifying those at risk. In addition, this will be the largest genotyped population of HCM available to correlate with comprehensive CMR and biomarker evaluation. This will allow unique opportunities to evaluate genotype-phenotype correlations and compare specific genetic subsets in a manner that has not been possible in the past. This study will also establish a predictive model that can be used to assess risk given a patient's combination of risk factors. This will help to select patients for future clinical trials to prevent SCD and HF. In addition, it will identify surrogate endpoints to monitor treatment response in HCM. In this way, the evidence base will be established in HCM to enable clinical trial design to reduce morbidity and mortality in HCM in a cost-effective manner.

3 OBJECTIVES The Specific Aim of this study is to develop a predictive model of cardiovascular outcomes in hypertrophic cardiomyopathy by: 1) using exploratory data mining methods to identify demographic, clinical, and novel CMR, genetic and biomarker variables associated with the outcomes and 2) develop a score from the predictive model that can be used to assess risk given a patient's combination of risk factors, thus establishing the evidence base to enable clinical trial design to reduce morbidity and mortality in HCM in a cost-effective manner.

To understand the relationship between these novel risk markers in HCM and clinical outcome, the investigators propose a natural history study of 2750 patients with clinically diagnosed HCM studied at baseline with collection of demographic data, clinical risk factors, as well as novel markers from CMR, genotyping, and serum biomarkers of collagen turnover and myocardial injury, enrolled over a 2-year period and followed for 3-5 years (mean of 4 years). The study will be powered to identify risk markers in a Cox model (imaging, serum, and genetic beyond standard clinical risk factors) with a hazard ratio of 1.5 or greater for the primary endpoint, which will be cardiac death (including SCD and HF death), aborted SCD (appropriate discharge of an implantable cardioverter-defibrillator), and need for heart transplantation. Secondary endpoints include all-cause mortality, ventricular tachyarrhythmias, hospitalization for heart failure, atrial fibrillation, and stroke. This study will enable establishment of a predictive model that will help to identify patients at risk as well as patients for future clinical trials to prevent SCD and HF. In addition, it will identify surrogate endpoints to monitor treatment response in HCM.

4 STUDY DESIGN 4.1 Selection of the Study Population

Patient characteristics:

2750 patients will be recruited and will include at least 51% females and 30% minorities.

Recruiting locations:

Inpatient or outpatient setting from 35-40 sites in North America and Europe

Serum Biomarker and DNA Samples Blood will be collected by peripheral venipuncture at enrollment as a source of serum and plasma for biomarker analysis, as well as a source of DNA for genetic testing. Fasting samples are requested but if this is not possible logistically, non-fasting samples can be acquired and noted as such. The investigators will also ask subjects to refrain from strenuous exercise for 24 hours prior to blood draw to decrease the potential for spuriously elevated biomarkers that reflect the influence of vigorous physical activity.

Cardiac MRI

A cardiac MRI will be performed and will take approximately one hour. 0.15mM of gadolinium contrast will be infused through an intravenous line during the MRI.

Full details of the protocol will be in the Study Manual. Laboratory Evaluations 5.1.1 Specimen Collection, Preparation, Handling and Shipping Biomarker samples (Full details are provided in the separate Biomarkers Technical Manual) 1. Collect 3, 10 ml tubes each of serum and K3 EDTA plasma in the fasting state (or indicated non-fasting if fasting samples cannot be obtained) DNA samples

Informed consent must be obtained by a member of the study team. 6.2 Baseline visit

Initial visit will be scheduled for study procedures as listed in #5 above. 6.3 Follow-up Visits Yearly telephone follow-up will be obtained for up to 5 years and review of applicable hospital or death records. Health status will be assessed with the SF-12 at this time.

10 STATISTICAL CONSIDERATIONS 10.1 Study Outcome Measures

The primary endpoint of this prospective study is the composite of cardiac death (SCD and HF death), aborted SCD including appropriate ICD firing, and need for heart transplantation.

10.2 Sample Size Considerations Assuming a 3-year event rate of 4.2% based on recently published literature10;11;26;27 and alpha error rate of 5%, 2500 patients provides over 90% power to detect a hazard ratio of 1.5 for at least one risk factor in the Cox model. An additional 250 (2750 total) patients will be enrolled to compensate for a projected dropout rate of 2% per year.

10.3 Participant Enrollment and Follow-Up 2750 enrollees, will be followed up to 5 years. 10.4 Analysis Plan Because the anticipated event rate is small (4-5%), and the number of potential predictive risk factors is large (20+), an exploratory, tree classification analysis will be used to identify the strongest predictors and eliminate any that are judged to have little or no predictive power. Based on the risk factors identified in the exploratory analysis, a regression prediction model will be developed from which a summary risk score for HCM can be calculated and easily used in clinical settings.

Tree-based data mining methods will be used to identify key variables in the prediction of the primary outcome, a composite of cardiac death (SCD and HF death), aborted SCD including appropriate ICD firing, and need for heart transplantation. Secondary endpoints will include all-cause mortality, ventricular tachyarrhythmias, septal myectomy or alcohol ablation, hospitalization for heart failure, atrial fibrillation, and stroke. Tree-based modeling is an exploratory technique for uncovering structure in multivariate data28;29. It is particularly useful for deriving prediction rules from a large number of screening variables or risk factors. Tree models can be used for both classification (binary outcome) and regression (continuous outcome). In either case, the collection of prediction rules (from the predictor variables) is displayed in the form of a tree. The terminology mimics that of trees: the root is the top node of the tree that displays the mean (regression) or proportion (classification) of the outcome for the entire sample. A split is the rule for creating new branches, and a leaf is a terminal node. Each node is a binary split of the predictor variable that contains two sub-groups of the sample with the largest possible difference between the groups. The same variable can appear in more than one level of the tree (recursive partitioning).

The advantage of tree-based models over linear and additive models is that they are more adept at capturing non-additive relationships and more easily uncover complex interactions between predictor variables. Continuous variables or categorical variables with more than two levels need not be dichotomized prior to the analysis - the tree algorithm determines the cutoff value that produces the most homogenous nodes. Missing data can be treated as a separate category. Initial tree models will include risk factors listed in the following table as well as demographic variables, age, sex, and race.

Clinical CMR Genetic Biomarkers Family history of HCM-related SCD LV ejection fraction MYH7 NT-pro BNP Unexplained recent syncope LV mass/mass index MYBPC3 Singulex cardiac troponin Massive LVH (wall thickness >30mm) Maximum diastolic wall thickness Thin filament mutations PICP Multiiple burst of nonsustained VT LGE extent (% of LV mass) Compound/double heterozygotes ST2 Hypotensive BP response to exercise Global extracellular volume fraction Sarcomere mutation negative The initial tree is reduced in size (pruned) by comparing misclassification error rates of smaller trees to larger trees to assess the reduction of the predictive ability of trees resulting from the pruning process. The goal is to identify the strongest predictors of HCM, any interactions of the predictors, and eliminate any factors that have little predictive power. While tree models give rapid identification of the strongest predictors and combinations of predictors, they do not usually result in clinically useful prediction models that generalize well to patients in other settings. Regression methods, using test-validation re-sampling procedures, will be used to develop the most valid model possible.

Using the demographic, clinical, imaging, biomarker and genetic measures, plus any interactions, identified in the tree analysis, Cox proportional hazards regression will be used to develop a predictive model from which a summary risk score can be calculated and applied in clinical settings. Parametric regression methods may be used if the proportional hazards assumption is not met. The regression model will be developed using a 100-fold validation procedure whereby 50% of the observations will be randomly selected for model development (test sample) and the remaining 50% used to assess the model's predictive ability (validation sample). This procedure will be repeated 100 times with a different random selection of test-validation samples for each iteration. The c-index will be calculated to assess model discrimination for both test and validation samples30. The ensemble of test-validation models provides an estimate of the attenuation of the c-index when the model is applied to a different sample as well as consistency of specific risk factors appearing in the models. A final model will be constructed that includes the combination of the strongest (high c-index and frequency of appearance) predictors. This model will then be applied to the entire sample to estimate regression coefficients and hazard ratios and calculate a summary risk score30. Nomograms will be constructed from the final model to provide predicted probabilities of the risk scores. To increase the ease of use of a nomogram, variables which are statistically significant but have little effect on prediction may be excluded. The model will also be used to develop a prediction algorithm that rapidly calculates risk on a hand-held electronic device. This analysis may also inform the design of clinical trials to evaluate therapeutic efficacy by providing effect sizes (hazard ratios) to calculate sample size requirements.

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Please refer to this study by its ClinicalTrials.gov identifier: NCT01915615